An electronic fraud network (EFN) acts as a data hub obtaining and/or capturing fraud-related or potentially fraud-related information from multiple products and installations. Once obtained and/or captured, the EFN analyzes the data, sanitizes portions of the data as necessary, and creates a unified feed for subsequent use by all installations. By way merely of example, fraud-related information might include a list of interne protocol (IP) addresses (or other identifiers) used to generate and/or further fraud-related activities. Additionally, fraud-related information can include a list of genuine IP addresses not associated with fraud-related activities. The EFN can obtain such lists from multiple providers such as various product installations and/or entities (also referred to herein as customers).
Using such information, the EFN can generate one unified list identifying each list entry as likely fraud-related or likely not fraud-related. The EFN may also, for example, incorporate a weight and/or risk measure per IP address associating a probability measure to each entry's identified status. In generating the unified list, the EFN commonly takes into account variables such as repetitions between sources (indicating a higher level of assurance) and conflicts between sources (indicating a lower level of assurance).
However, existing approaches utilizing such techniques face challenges. For example, customers may repeatedly provide low-quality or inaccurate data. Such data may include an improper format, erroneous classifications of data (for example, genuine/non-fraudulent data marked as fraud-related data), etc. Accordingly, a need exists for the capability to automatically provide feedback to customers regarding the quality of information provided to an EFN so that the customers may provide higher quality data in the future.